One Thing After Another: Why the Passage of Time Is Not an Illusion

Does time seem to pass, even though it doesn’t, really? Many philosophers think the answer is ‘Yes’—at least when ‘time’s passing’ is understood in a particular way. They take time’s passing to be a process by which each time in turn acquires a special status, such as the status of being the only time that exists, or being the only time that is present. This chapter suggests that, on the contrary, all we perceive is temporal succession, one thing after another, a notion to which modern physics is not inhospitable. The contents of perception are best described in terms of ‘before’ and ‘after’, rather than ‘past’, ‘present, and ‘future’

Why do we remember? The communicative function of episodic memory

Episodic memory has been analyzed in a number of different ways in both philosophy and psychology, and most controversy has centered on its self-referential, ‘autonoetic’ character. Here, we offer a comprehensive characterization of episodic memory in representational terms, and propose a novel functional account on this basis. We argue that episodic memory should be understood as a distinctive epistemic attitude taken towards an event simulation. On this view, episodic memory has a metarepresentational format and should not be equated with beliefs about the past. Instead, empirical findings suggest that the contents of human episodic memory are often constructed in the service of the explicit justification of such beliefs. Existing accounts of episodic memory function that have focused on explaining its constructive character through its role in ‘future-oriented mental time travel’ neither do justice to its capacity to ground veridical beliefs about the past nor to its representational format. We provide an account of the metarepresentational structure of episodic memory in terms of its role in communicative interaction. The generative nature of recollection allows us to represent and communicate the reasons for why we hold certain beliefs about the past. In this process, autonoesis corresponds to the capacity to determine when and how to assert epistemic authority in making claims about the past. A domain where such claims are indispensable are human social engagements. Such engagements commonly require the justification of entitlements and obligations, which is often possible only by explicit reference to specific past events

Do we (seem to) perceive passage?

I examine some recent claims put forward by L. A. Paul, Barry Dainton and Simon Prosser, to the effect that perceptual experiences of movement and change involve an (apparent) experience of ‘passage’, in the sense at issue in debates about the metaphysics of time. Paul, Dainton and Prosser all argue that this supposed feature of perceptual experience – call it a phenomenology of passage – is illusory, thereby defending the view that there is no such a thing as passage, conceived of as a feature of mind-independent reality. I suggest that in fact there is no such phenomenology of passage in the first place. There is, however, a specific structural aspect of the phenomenology of perceptual experiences of movement and change that can explain how one might mistakenly come to the belief that such experiences do involve a phenomenology of passage

Quality management in heavy duty manufacturing industry: TQM vs. Six Sigma

‘Is TQM a management fad?’ This question has been extensively documented in the quality management literature; and will be tackled in this research though a critical literature review on the area. ‘TQM versus Six-Sigma’ debate, which has also been a fundamental challenge in this research filed, is addressed by a thematic and chronological review on the peer papers. To evaluate this challenge in practice, a primary research in heavy duty machinery production industry have been conducted using a case-study on, J C Bamford Excavators Ltd (JCB), the largest European construction machinery producer. The result highlights that TQM is a natural foundation to build up Six-Sigma upon; and not surprisingly the quality yield in a TQM approach complemented by Six-sigma is far higher and more stable than when TQM with no Six-Sigma focus is being put in place; thus presenting the overall finding that TQM and Six Sigma are compliments, not substitutes. The study will be concluded with an overview on quality management approaches in the heavy duty manufacturing industry to highlight the way forward for the industry

On the combination of omics data for prediction of binary outcomes

Enrichment of predictive models with new biomolecular markers is an important task in high-dimensional omic applications. Increasingly, clinical studies include several sets of such omics markers available for each patient, measuring different levels of biological variation. As a result, one of the main challenges in predictive research is the integration of different sources of omic biomarkers for the prediction of health traits. We review several approaches for the combination of omic markers in the context of binary outcome prediction, all based on double cross-validation and regularized regression models. We evaluate their performance in terms of calibration and discrimination and we compare their performance with respect to single-omic source predictions. We illustrate the methods through the analysis of two real datasets. On the one hand, we consider the combination of two fractions of proteomic mass spectrometry for the calibration of a diagnostic rule for the detection of early-stage breast cancer. On the other hand, we consider transcriptomics and metabolomics as predictors of obesity using data from the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort, from Finland

Madness decolonized?: Madness as transnational identity in Gail Hornstein’s Agnes’s Jacket

The US psychologist Gail Hornstein’s monograph Agnes’s Jacket: A Psychologist’s Search for the Meanings of Madness (2009) is an important intervention in the identity politics of the mad movement. Hornstein offers a resignified vision of mad identity that embroiders the central trope of an “anti-colonial” struggle to reclaim the experiential world “colonized” by psychiatry. A series of literal and figurative appeals make recourse to the inner world and (corresponding) cultural world of the mad, as well as to the ethno-symbolic cultural materials of dormant nationhood. This rhetoric is augmented by a model in which the mad comprise a diaspora without an origin, coalescing into a single transnational community. The mad are also depicted as persons displaced from their metaphorical homeland, the “inner” world “colonized” by the psychiatric regime. There are a number of difficulties with Hornstein’s rhetoric, however. Her “ethnicity-and-rights” response to the oppression of the mad is symptomatic of Western parochialism, while her proposed transmutation of putative psychopathology from limit upon identity to parameter of successful identity is open to contestation. Moreover, unless one accepts Hornstein’s porous vision of mad identity, her self-ascribed insider status in relation to the mad community may present a problematic “re-colonization” of mad experience

The geography of recent genetic ancestry across Europe

The recent genealogical history of human populations is a complex mosaic formed by individual migration, large-scale population movements, and other demographic events. Population genomics datasets can provide a window into this recent history, as rare traces of recent shared genetic ancestry are detectable due to long segments of shared genomic material. We make use of genomic data for 2,257 Europeans (the POPRES dataset) to conduct one of the first surveys of recent genealogical ancestry over the past three thousand years at a continental scale. We detected 1.9 million shared genomic segments, and used the lengths of these to infer the distribution of shared ancestors across time and geography. We find that a pair of modern Europeans living in neighboring populations share around 10-50 genetic common ancestors from the last 1500 years, and upwards of 500 genetic ancestors from the previous 1000 years. These numbers drop off exponentially with geographic distance, but since genetic ancestry is rare, individuals from opposite ends of Europe are still expected to share millions of common genealogical ancestors over the last 1000 years. There is substantial regional variation in the number of shared genetic ancestors: especially high numbers of common ancestors between many eastern populations likely date to the Slavic and/or Hunnic expansions, while much lower levels of common ancestry in the Italian and Iberian peninsulas may indicate weaker demographic effects of Germanic expansions into these areas and/or more stably structured populations. Recent shared ancestry in modern Europeans is ubiquitous, and clearly shows the impact of both small-scale migration and large historical events. Population genomic datasets have considerable power to uncover recent demographic history, and will allow a much fuller picture of the close genealogical kinship of individuals across the world.Comment: Full size figures available from http://www.eve.ucdavis.edu/~plralph/research.html; or html version at http://ralphlab.usc.edu/ibd/ibd-paper/ibd-writeup.xhtm

Assessing the impact of a health intervention via user-generated Internet content

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of user-generated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the prevalence of a health event in a population from Internet data. This model is applied to identify control location groups that correlate historically with the areas, where a specific intervention campaign has taken place. We then determine the impact of the intervention by inferring a projection of the disease rates that could have emerged in the absence of a campaign. Our case study focuses on the influenza vaccination program that was launched in England during the 2013/14 season, and our observations consist of millions of geo-located search queries to the Bing search engine and posts on Twitter. The impact estimates derived from the application of the proposed statistical framework support conventional assessments of the campaign

Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining

Purpose: To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction signals obtained via longitudinal observational data. Methods: We considered six drug families that are commonly associated with myocardial infarction in observational healthcare data, but where the causal relationship ground truth is known (adverse drug reaction or not). We applied emergent pattern mining to find itemsets of drugs and medical events that are associated with the development of myocardial infarction. These are the candidate confounding interaction terms. We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms. Results: The methodology was able to account for signals generated due to confounding and a cox regression with elastic net regularisation correctly ranking the drug families known to be true adverse drug reactions above those that are not. This was not the case without the inclusion of the candidate confounding interaction terms, where confounding leads to a non-adverse drug reaction being ranked highest. Conclusions: The methodology is efficient, can identify high-order confounding interactions and does not require expert input to specify outcome specific confounders, so it can be applied for any outcome of interest to quickly refine its signals. The proposed method shows excellent potential to overcome some forms of confounding and therefore reduce the false positive rate for signal analysis using longitudinal data

The use of the Kalman filter in the automated segmentation of EIT lung images

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging